PyTorch, Tensorflow 사용자를 위한 Hands-On
- NVIDIA 드라이버 설치
- CUDA 버전을 확인
- CUDA를 설치
- cuDNN 설치
- 설치 확인
- Software & Updator > Settings > Additional Drivers
- Using NVIDIA driver metapackage from nvidia-driver-525
- terminal >
nvidia-smi
$ nvidia-smi
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.60.11 Driver Version: 525.60.11 CUDA Version: 12.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... Off | 00000000:01:00.0 Off | N/A |
| N/A 50C P3 6W / N/A | 5MiB / 4096MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 1802 G /usr/lib/xorg/Xorg 4MiB |
+-----------------------------------------------------------------------------+
- Tensorflow <= 11.8
- PyTorch <= 11.8
- CUDA 저장소(NVIDIA)에서 11.8 버전을 다운로드 후 실행
$ wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run
$ sudo sh cuda_11.8.0_520.61.05_linux.run
Continue를 선택해서 계속 진행
┌──────────────────────────────────────────────────────────────────────────────┐
│ Existing package manager installation of the driver found. It is strongly │
│ recommended that you remove this before continuing. │
│ Abort │
│ Continue │
│ │
│ │
│ │
│ Up/Down: Move | 'Enter': Select │
└──────────────────────────────────────────────────────────────────────────────┘
accept를 입력
┌──────────────────────────────────────────────────────────────────────────────┐
│ End User License Agreement │
│ -------------------------- │
│ │
│ NVIDIA Software License Agreement and CUDA Supplement to │
│ Software License Agreement. Last updated: October 8, 2021 │
│ │
│ The CUDA Toolkit End User License Agreement applies to the │
│ NVIDIA CUDA Toolkit, the NVIDIA CUDA Samples, the NVIDIA │
│ Display Driver, NVIDIA Nsight tools (Visual Studio Edition), │
│ and the associated documentation on CUDA APIs, programming │
│ model and development tools. If you do not agree with the │
│ terms and conditions of the license agreement, then do not │
│ download or use the software. │
│ │
│ Last updated: October 8, 2021. │
│ │
│ │
│ Preface │
│ ------- │
│ │
│──────────────────────────────────────────────────────────────────────────────│
│ Do you accept the above EULA? (accept/decline/quit): │
│ │
└──────────────────────────────────────────────────────────────────────────────┘
Driver를 제외하고Install진행
┌──────────────────────────────────────────────────────────────────────────────┐
│ CUDA Installer │
│ - [ ] Driver │
│ [ ] 520.61.05 │
│ + [X] CUDA Toolkit 11.8 │
│ [X] CUDA Demo Suite 11.8 │
│ [X] CUDA Documentation 11.8 │
│ - [ ] Kernel Objects │
│ [ ] nvidia-fs │
│ Options │
│ Install │
│ │
│ │
│ │
│ │
│ │
│ │
│ │
│ │
│ │
│ │
│ │
│ │
│ Up/Down: Move | Left/Right: Expand | 'Enter': Select | 'A': Advanced options │
└──────────────────────────────────────────────────────────────────────────────┘
- 설치 후 아래와 같은 안내화면 출력
===========
= Summary =
===========
Driver: Not Selected
Toolkit: Installed in /usr/local/cuda-11.8/
Please make sure that
- PATH includes /usr/local/cuda-11.8/bin
- LD_LIBRARY_PATH includes /usr/local/cuda-11.8/lib64, or, add /usr/local/cuda-11.8/lib64 to /etc/ld.so.conf and run ldconfig as root
To uninstall the CUDA Toolkit, run cuda-uninstaller in /usr/local/cuda-11.8/bin
***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least 520.00 is required for CUDA 11.8 functionality to work.
To install the driver using this installer, run the following command, replacing <CudaInstaller> with the name of this run file:
sudo <CudaInstaller>.run --silent --driver
Logfile is /var/log/cuda-installer.log
- cuDNN 저장소(NVIDIA)에서
cuDNN을 다운로드 Download cuDNN v8.6.0 (October 3rd, 2022), for CUDA 11.x선택- 11.x 버전 중 최신을 선택
Local Installer for Linux x86_64 (Tar)를 다운로드- 로그인 과정이 필요
tar xvf cudnn-linux-x86_64-8.6.0.163_cuda11-archive.tar.xz로 압축 해제cd cudnn-linux-x86_64-8.6.0.163_cuda11-archive로 이동cd include>sudo cp *.* /usr/local/cuda/include>cd ..cd lib>suco cp *.* /usr/local/cuda/lib64> 'cd ..`cd /usr/local/cuda-11.8/targets/x86_64-linux/lib- sudo ln -sf libcudnn_adv_train.so.8.6.0 libcudnn_adv_train.so.8
- sudo ln -sf libcudnn_adv_train.so.8 libcudnn_adv_train.so
- sudo ln -sf libcudnn_ops_infer.so.8.6.0 libcudnn_ops_infer.so.8
- sudo ln -sf libcudnn_ops_infer.so.8 libcudnn_ops_infer.so
- sudo ln -sf libcudnn_cnn_train.so.8.6.0 libcudnn_cnn_train.so.8
- sudo ln -sf libcudnn_cnn_train.so.8 libcudnn_cnn_train.so
- sudo ln -sf libcudnn_adv_infer.so.8.6.0 libcudnn_adv_infer.so.8
- sudo ln -sf libcudnn_adv_infer.so.8 libcudnn_adv_infer.so
- sudo ln -sf libcudnn_ops_train.so.8.6.0 libcudnn_ops_train.so.8
- sudo ln -sf libcudnn_ops_train.so.8 libcudnn_ops_train.so
- sudo ln -sf libcudnn_cnn_infer.so.8.6.0 libcudnn_cnn_infer.so.8
- sudo ln -sf libcudnn_cnn_infer.so.8 libcudnn_cnn_infer.so
- sudo ln -sf libcudnn.so.8.6.0 libcudnn.so.8
- sudo ln -sf libcudnn.so.8 libcudnn_cnn_infer.so
.bashrc나.zshrc에 아래 명령어를 작성
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.8/lib64
deviceQuery를 사용해서 설치를 확인
$ /usr/local/cuda-11.8/extras/demo_suite/deviceQuery
/usr/local/cuda-11.8/extras/demo_suite/deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "NVIDIA GeForce GTX 1650"
CUDA Driver Version / Runtime Version 12.0 / 11.8
CUDA Capability Major/Minor version number: 7.5
Total amount of global memory: 3906 MBytes (4095934464 bytes)
(16) Multiprocessors, ( 64) CUDA Cores/MP: 1024 CUDA Cores
GPU Max Clock rate: 1560 MHz (1.56 GHz)
Memory Clock rate: 4001 Mhz
Memory Bus Width: 128-bit
L2 Cache Size: 1048576 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 1024
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 3 copy engine(s)
Run time limit on kernels: Yes
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
Device supports Unified Addressing (UVA): Yes
Device supports Compute Preemption: Yes
Supports Cooperative Kernel Launch: Yes
Supports MultiDevice Co-op Kernel Launch: Yes
Device PCI Domain ID / Bus ID / location ID: 0 / 1 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 12.0, CUDA Runtime Version = 11.8, NumDevs = 1, Device0 = NVIDIA GeForce GTX 1650
Result = PASS